library(Seurat)
library(ggplot2)
library(magrittr)
library(dplyr)
library(tibble)
library(stringr)
library(modplots)
broad_order <- c("progenitors",
"FP",
"RP",
"FP/RP",
"neurons",
"OPC",
"MFOL",
"pericytes",
"microglia",
"blood",
"vasculature"
)
se_path <- c("Gg_ctrl_int_seurat_250723",
"Gg_lumb_int_seurat_250723")
int_path <- "Gg_ctrl_lumb_int_seurat_250723"
my.meta <- list()
annot <- list()
for (i in seq(se_path)) {
# load the data sets
my.se <- readRDS(paste0("~/spinal_cord_paper/data/", se_path[i], ".rds"))
annot[[i]] <- read.csv(list.files("~/spinal_cord_paper/annotations",
pattern = str_remove(se_path[i], "_seurat_\\d{6}"),
full.names = TRUE))
if(length(table(annot[[i]]$number)) != length(table(my.se$seurat_clusters))) {
stop("Number of clusters must be identical!")
}
# rename for left join
annot[[i]] <- annot[[i]] %>%
mutate(fine = paste(fine, number, sep = "_")) %>%
mutate(number = factor(number, levels = 1:nrow(annot[[i]]))) %>%
rename(seurat_clusters = number)
ord_levels <- annot[[i]]$fine[order(match(annot[[i]]$broad, broad_order))]
# add cluster annotation to meta data
my.meta[[i]] <- my.se@meta.data %>%
rownames_to_column("rowname") %>%
left_join(annot[[i]], by = "seurat_clusters") %>%
mutate(fine = factor(fine, levels = ord_levels)) %>%
mutate(seurat_clusters = factor(seurat_clusters, levels = str_extract(ord_levels, "\\d{1,2}$"))) %>%
column_to_rownames("rowname")
}
rm(my.se)
my.se <- readRDS(paste0("~/spinal_cord_paper/data/", int_path, ".rds"))
annot_int <- read.csv(list.files("~/spinal_cord_paper/annotations",
pattern = str_remove(int_path, "_seurat_\\d{6}"),
full.names = TRUE))
if(length(table(annot_int$number)) != length(table(my.se$seurat_clusters))) {
stop("Number of clusters must be identical!")
}
# rename for left join
annot_int <- annot_int %>%
mutate(fine = paste(fine, number, sep = "_")) %>%
mutate(number = factor(number, levels = 1:nrow(annot_int))) %>%
rename(seurat_clusters = number)
ord_levels <- annot_int$fine[order(match(annot_int$broad, broad_order))]
# add cluster annotation to meta data
my.se@meta.data <- my.se@meta.data %>%
rownames_to_column("rowname") %>%
left_join(annot_int, by = "seurat_clusters") %>%
mutate(fine = factor(fine, levels = ord_levels)) %>%
mutate(seurat_clusters = factor(seurat_clusters, levels = str_extract(ord_levels, "\\d{1,2}$"))) %>%
column_to_rownames("rowname")
clust_col <- read.csv("~/spinal_cord_paper/annotations/broad_cluster_marker_colors.csv")
broad_cols <- clust_col %>%
filter(broad_cluster %in% annot_int$broad) %>%
pull(color)
DimPlot(my.se, reduction = "tsne", group.by = "broad", label = TRUE, pt.size = 0.5, cols = broad_cols)
The cell IDs of the poly samples got the appendices _3 and _4. Therefore the labels from the previous integration of poly 1 and 2 get switched to 3 and 4, so the cluster annotations can be transfered.
table(stringi::stri_sub(rownames(my.meta[[1]]), -2,-1))
##
## _1 _2
## 2474 5537
table(stringi::stri_sub(rownames(my.meta[[2]]), -2,-1))
##
## _1 _2
## 2487 4599
table(stringi::stri_sub(rownames(my.se@meta.data), -2,-1))
##
## _1 _2 _3 _4
## 2474 5537 2487 4599
my.meta[[2]] <- my.meta[[2]] %>%
tibble::rownames_to_column("cell_ID") %>%
dplyr::mutate(cell_ID = str_replace(cell_ID, "_1", "_3")) %>%
dplyr::mutate(cell_ID = str_replace(cell_ID, "_2", "_4") ) %>%
tibble::column_to_rownames("cell_ID")
identical(c(rownames(my.meta[[1]]), rownames(my.meta[[2]])), rownames(my.se@meta.data))
## [1] TRUE
cell_annot <- rbind(my.meta[[1]], my.meta[[2]]) %>%
dplyr::mutate(sample = substr(orig.ident, 4, 7)) %>%
dplyr::mutate(annot_sample = paste(fine, sample, sep = "_")) %>%
dplyr::mutate(broad_sample = paste(broad, sample, sep = "_"))
my.se <- AddMetaData(my.se, cell_annot[, c("sample","annot_sample", "broad_sample")])
brach_poly_combined_labels <- cell_annot[, c("sample","annot_sample", "broad_sample")]
saveRDS(brach_poly_combined_labels, file = "~/spinal_cord_paper/annotations/ctrl_lumb_int_combined_labels.rds")
p1 <- DimPlot(
my.se,
group.by = "annot_sample",
reduction = "tsne",
label = TRUE,
repel = TRUE
) +
NoLegend()
p2 <- DimPlot(
my.se,
group.by = "annot_sample",
reduction = "tsne",
label = FALSE,
split.by = "sample",
repel = TRUE
) +
NoLegend()
p3 <- DimPlot(
my.se,
group.by = "broad_sample",
reduction = "tsne",
label = TRUE,
repel = TRUE
)
p1 + p2 + p3
## Sister Pair DE
Here we run DE analasys between terminal sister pairs of different origin from the heatmap in spinal_cord_paper/markdown/heatmap_spearman_ctrl_lumb_poly_int.html.
# sister pairs
sis_pairs <- data.frame(
"ctrl" = c(16, 6, 20, 11, 8),
"lumb" = c(24, 20, 20, 15, 15)
)
# cell type annotations
sis_ctrl <- annot[[1]][sis_pairs$ctrl,] %>%
droplevels() %>%
mutate(fine = paste0(fine, "_ctrl"))
sis_lumb <- annot[[2]][sis_pairs$lumb,] %>%
droplevels() %>%
mutate(fine = paste0(fine, "_lumb"))
sis_annot <- cbind(sis_ctrl, sis_lumb) %>% tibble::remove_rownames()
colnames(sis_annot) <- paste0(c(rep("ctrl_",3),rep("lumb_",3)),
colnames(sis_annot))
sis_annot
Idents(my.se) <- "annot_sample"
sis_markers <- list()
for (i in seq(nrow(sis_annot))) {
sis_markers[[i]] <- FindMarkers(
my.se,
ident.1 = sis_annot[i, "ctrl_fine"],
ident.2 = sis_annot[i, "lumb_fine"],
assay = "RNA",
verbose = FALSE,
only.pos = FALSE,
min.pct = 0.25,
logfc.threshold = 0.25,
latent.vars = c("CC.Difference.seurat"),
test.use = "MAST"
) %>%
tibble::rownames_to_column("Gene.stable.ID") %>%
dplyr::left_join(gnames, by = "Gene.stable.ID") %>%
dplyr::arrange(-avg_log2FC) %>%
dplyr::filter(p_val_adj < 0.05) %>%
dplyr::filter(abs(avg_log2FC) > 0.5) %>%
dplyr::mutate(delta_pct = pct.1 - pct.2)
}
names(sis_markers) <- paste0(sis_annot$ctrl_fine, "-vs-", sis_annot$lumb_fine)
saveRDS(sis_markers, file = "~/spinal_cord_paper/data/Gg_ctrl_lumb_sis_markers.rds")
se_path <- c("Gg_ctrl_int_seurat_250723",
"Gg_poly_int_seurat_250723")
int_path <- "Gg_ctrl_poly_int_seurat_250723"
my.meta <- list()
annot <- list()
for (i in seq(se_path)) {
# load the data sets
my.se <- readRDS(paste0("~/spinal_cord_paper/data/", se_path[i], ".rds"))
annot[[i]] <- read.csv(list.files("~/spinal_cord_paper/annotations",
pattern = str_remove(se_path[i], "_seurat_\\d{6}"),
full.names = TRUE))
if(length(table(annot[[i]]$number)) != length(table(my.se$seurat_clusters))) {
stop("Number of clusters must be identical!")
}
# rename for left join
annot[[i]] <- annot[[i]] %>%
mutate(fine = paste(fine, number, sep = "_")) %>%
mutate(number = factor(number, levels = 1:nrow(annot[[i]]))) %>%
rename(seurat_clusters = number)
ord_levels <- annot[[i]]$fine[order(match(annot[[i]]$broad, broad_order))]
# add cluster annotation to meta data
my.meta[[i]] <- my.se@meta.data %>%
rownames_to_column("rowname") %>%
left_join(annot[[i]], by = "seurat_clusters") %>%
mutate(fine = factor(fine, levels = ord_levels)) %>%
mutate(seurat_clusters = factor(seurat_clusters, levels = str_extract(ord_levels, "\\d{1,2}$"))) %>%
column_to_rownames("rowname")
}
rm(my.se)
my.se <- readRDS(paste0("~/spinal_cord_paper/data/", int_path, ".rds"))
annot_int <- read.csv(list.files("~/spinal_cord_paper/annotations",
pattern = str_remove(int_path, "_seurat_\\d{6}"),
full.names = TRUE))
if(length(table(annot_int$number)) != length(table(my.se$seurat_clusters))) {
stop("Number of clusters must be identical!")
}
# rename for left join
annot_int <- annot_int %>%
mutate(fine = paste(fine, number, sep = "_")) %>%
mutate(number = factor(number, levels = 1:nrow(annot_int))) %>%
rename(seurat_clusters = number)
ord_levels <- annot_int$fine[order(match(annot_int$broad, broad_order))]
# add cluster annotation to meta data
my.se@meta.data <- my.se@meta.data %>%
rownames_to_column("rowname") %>%
left_join(annot_int, by = "seurat_clusters") %>%
mutate(fine = factor(fine, levels = ord_levels)) %>%
mutate(seurat_clusters = factor(seurat_clusters, levels = str_extract(ord_levels, "\\d{1,2}$"))) %>%
column_to_rownames("rowname")
broad_cols <- clust_col %>%
filter(broad_cluster %in% annot_int$broad) %>%
pull(color)
DimPlot(my.se, reduction = "tsne", group.by = "broad", label = TRUE, pt.size = 0.5, cols = broad_cols)
The cell IDs of the poly samples got the appendices _3 and _4. Therefore the labels from the previous integration of poly 1 and 2 get switched to 3 and 4, so the cluster annotations can be transfered.
table(stringi::stri_sub(rownames(my.meta[[1]]), -2,-1))
##
## _1 _2
## 2474 5537
table(stringi::stri_sub(rownames(my.meta[[2]]), -2,-1))
##
## _1 _2
## 2592 4334
table(stringi::stri_sub(rownames(my.se@meta.data), -2,-1))
##
## _1 _2 _3 _4
## 2474 5537 2592 4334
my.meta[[2]] <- my.meta[[2]] %>%
tibble::rownames_to_column("cell_ID") %>%
dplyr::mutate(cell_ID = str_replace(cell_ID, "_1", "_3")) %>%
dplyr::mutate(cell_ID = str_replace(cell_ID, "_2", "_4") ) %>%
tibble::column_to_rownames("cell_ID")
identical(c(rownames(my.meta[[1]]), rownames(my.meta[[2]])), rownames(my.se@meta.data))
## [1] TRUE
cell_annot <- rbind(my.meta[[1]], my.meta[[2]]) %>%
dplyr::mutate(sample = substr(orig.ident, 4, 7)) %>%
dplyr::mutate(annot_sample = paste(fine, sample, sep = "_")) %>%
dplyr::mutate(broad_sample = paste(broad, sample, sep = "_"))
my.se <- AddMetaData(my.se, cell_annot[, c("sample","annot_sample", "broad_sample")])
brach_poly_combined_labels <- cell_annot[, c("sample","annot_sample", "broad_sample")]
saveRDS(brach_poly_combined_labels, file = "~/spinal_cord_paper/annotations/ctrl_poly_int_combined_labels.rds")
p1 <- DimPlot(
my.se,
group.by = "annot_sample",
reduction = "tsne",
label = TRUE,
repel = TRUE
) +
NoLegend()
p2 <- DimPlot(
my.se,
group.by = "annot_sample",
reduction = "tsne",
label = FALSE,
split.by = "sample",
repel = TRUE
) +
NoLegend()
p3 <- DimPlot(
my.se,
group.by = "broad_sample",
reduction = "tsne",
label = TRUE,
repel = TRUE
)
p1 + p2 + p3
## Sister Pair DE
Here we run DE analasys between terminal sister pairs of different origin from the heatmap in spinal_cord_paper/markdown/heatmap_spearman_ctrl_lumb_poly_int.html.
# sister pairs
sis_pairs <- data.frame(
"ctrl" = c(16, 16, 20, 11, 8, 6),
"poly" = c(14, 26, 22, 15, 15, 6)
)
# cell type annotations
sis_ctrl <- annot[[1]][sis_pairs$ctrl,] %>%
droplevels() %>%
mutate(fine = paste0(fine, "_ctrl"))
sis_poly <- annot[[2]][sis_pairs$poly,] %>%
droplevels() %>%
mutate(fine = paste0(fine, "_poly"))
sis_annot <- cbind(sis_ctrl, sis_poly) %>% tibble::remove_rownames()
colnames(sis_annot) <- paste0(c(rep("ctrl_",3),rep("poly_",3)),
colnames(sis_annot))
sis_annot
Idents(my.se) <- "annot_sample"
sis_markers <- list()
for (i in seq(nrow(sis_annot))) {
sis_markers[[i]] <- FindMarkers(
my.se,
ident.1 = sis_annot[i, "ctrl_fine"],
ident.2 = sis_annot[i, "poly_fine"],
assay = "RNA",
verbose = FALSE,
only.pos = FALSE,
min.pct = 0.25,
logfc.threshold = 0.25,
latent.vars = c("CC.Difference.seurat"),
test.use = "MAST"
) %>%
tibble::rownames_to_column("Gene.stable.ID") %>%
dplyr::left_join(gnames, by = "Gene.stable.ID") %>%
dplyr::arrange(-avg_log2FC) %>%
dplyr::filter(p_val_adj < 0.05) %>%
dplyr::filter(abs(avg_log2FC) > 0.5) %>%
dplyr::mutate(delta_pct = pct.1 - pct.2)
}
names(sis_markers) <- paste0(sis_annot$ctrl_fine, "-vs-", sis_annot$poly_fine)
saveRDS(sis_markers, file = "~/spinal_cord_paper/data/Gg_ctrl_poly_sis_markers.rds")
# Date and time of Rendering
Sys.time()
## [1] "2024-06-07 10:26:56 CEST"
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /scicore/soft/apps/OpenBLAS/0.3.1-GCC-7.3.0-2.30/lib/libopenblas_sandybridgep-r0.3.1.so
##
## locale:
## [1] en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] modplots_1.0.0 stringr_1.4.0 tibble_3.1.8 dplyr_1.0.10
## [5] magrittr_2.0.1 ggplot2_3.3.3 SeuratObject_4.0.2 Seurat_4.0.5
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.6 igraph_1.2.6
## [3] lazyeval_0.2.2 sp_1.4-5
## [5] splines_4.1.0 listenv_0.8.0
## [7] scattermore_0.7 GenomeInfoDb_1.28.0
## [9] digest_0.6.27 htmltools_0.5.1.1
## [11] fansi_0.5.0 memoise_2.0.0
## [13] tensor_1.5 cluster_2.1.2
## [15] ROCR_1.0-11 globals_0.16.2
## [17] Biostrings_2.60.0 matrixStats_0.58.0
## [19] spatstat.sparse_3.0-0 prettyunits_1.1.1
## [21] colorspace_2.0-1 blob_1.2.1
## [23] ggrepel_0.9.1 xfun_0.34
## [25] crayon_1.4.1 RCurl_1.98-1.3
## [27] jsonlite_1.7.2 spatstat.data_3.0-0
## [29] survival_3.2-11 zoo_1.8-9
## [31] glue_1.6.2 polyclip_1.10-0
## [33] gtable_0.3.0 zlibbioc_1.38.0
## [35] XVector_0.32.0 leiden_0.3.9
## [37] DelayedArray_0.18.0 SingleCellExperiment_1.14.1
## [39] future.apply_1.7.0 BiocGenerics_0.38.0
## [41] abind_1.4-5 scales_1.1.1
## [43] pheatmap_1.0.12 DBI_1.1.1
## [45] miniUI_0.1.1.1 Rcpp_1.0.7
## [47] progress_1.2.2 viridisLite_0.4.0
## [49] xtable_1.8-4 reticulate_1.22
## [51] spatstat.core_2.1-2 bit_4.0.4
## [53] stats4_4.1.0 htmlwidgets_1.5.3
## [55] httr_1.4.2 RColorBrewer_1.1-2
## [57] ellipsis_0.3.2 ica_1.0-2
## [59] farver_2.1.0 pkgconfig_2.0.3
## [61] sass_0.4.0 uwot_0.1.10
## [63] deldir_1.0-6 utf8_1.2.1
## [65] labeling_0.4.2 tidyselect_1.2.0
## [67] rlang_1.0.6 reshape2_1.4.4
## [69] later_1.2.0 AnnotationDbi_1.54.0
## [71] munsell_0.5.0 tools_4.1.0
## [73] cachem_1.0.5 cli_3.4.1
## [75] generics_0.1.3 RSQLite_2.2.7
## [77] ggridges_0.5.3 org.Gg.eg.db_3.13.0
## [79] evaluate_0.20 fastmap_1.1.0
## [81] yaml_2.2.1 goftest_1.2-2
## [83] knitr_1.41 bit64_4.0.5
## [85] fitdistrplus_1.1-6 purrr_0.3.4
## [87] RANN_2.6.1 KEGGREST_1.32.0
## [89] pbapply_1.4-3 future_1.30.0
## [91] nlme_3.1-152 mime_0.10
## [93] compiler_4.1.0 plotly_4.10.0
## [95] png_0.1-7 spatstat.utils_3.0-1
## [97] bslib_0.2.5.1 stringi_1.6.2
## [99] highr_0.9 lattice_0.20-44
## [101] Matrix_1.3-3 vctrs_0.5.1
## [103] pillar_1.8.1 lifecycle_1.0.3
## [105] spatstat.geom_3.0-3 lmtest_0.9-38
## [107] jquerylib_0.1.4 RcppAnnoy_0.0.19
## [109] data.table_1.14.0 cowplot_1.1.1
## [111] bitops_1.0-7 irlba_2.3.3
## [113] GenomicRanges_1.44.0 httpuv_1.6.1
## [115] patchwork_1.1.1 R6_2.5.0
## [117] promises_1.2.0.1 KernSmooth_2.23-20
## [119] gridExtra_2.3 IRanges_2.26.0
## [121] parallelly_1.33.0 codetools_0.2-18
## [123] MASS_7.3-54 assertthat_0.2.1
## [125] MAST_1.18.0 SummarizedExperiment_1.22.0
## [127] withr_2.4.2 sctransform_0.3.3
## [129] S4Vectors_0.30.0 GenomeInfoDbData_1.2.6
## [131] hms_1.1.0 mgcv_1.8-35
## [133] parallel_4.1.0 grid_4.1.0
## [135] rpart_4.1-15 tidyr_1.1.3
## [137] rmarkdown_2.17 MatrixGenerics_1.4.0
## [139] Rtsne_0.15 Biobase_2.52.0
## [141] shiny_1.6.0